FLAT：前饋潛在三角形潑濺技術用於幾何精確場景生成

摘要

從單張影像生成可探索的3D場景，需要具備強大的生成先驗知識，以及適合下游應用的精確幾何表示。目前的影片擴散模型能夠生成高品質的內容，並在潛在空間中隱式編碼多視角的幾何結構。然而，現有的前饋式潛在場景解碼器通常會輸出缺乏明確表面的體積式3D高斯函數，限制了其在模擬或標準圖形管線中的應用。這促使我們解碼出不僅可渲染、且更接近於明確幾何資產的表面對齊基元。我們提出的問題是：壓縮後的影片擴散潛在表示，能否在單次前向傳遞中直接映射到明確的表面基元？為此，我們引入了FLAT，並首次證明三角形（Triangle Splats）可從影片擴散潛在表示中直接解碼。與解碼3D高斯函數相比，預測平面基元因對基元方向高度敏感而更加困難，往往導致梯度流不穩定。FLAT透過兩個關鍵要素解決此問題：一個用於三角形回歸的射線中心旋轉參數化方法，以及一個新穎的乘積窗口函數，用於改善可微三角形渲染過程中的梯度流。在標準基準測試中，FLAT在幾何精度上顯著優於最先進的前饋式基線方法，同時保持具有競爭力的視覺品質。我們進一步證明，一個輕量級的測試時優化步驟，可將預測的三角形集合轉換為完全非透明的、可直接用於遊戲引擎的表示，支援即時渲染。透過在相同訓練設定下評估3DGS、2DGS與三角形濺射變體，我們首次系統性地分析了前饋式場景生成中各種表示的取捨。專案頁面請見 https://flat-splat.github.io

English

Generating explorable 3D scenes from a single image requires strong generative priors and accurate geometric representations suitable for downstream use. Current video diffusion models offer high-quality generation and implicitly encode multi-view geometric structure in latent space. However, existing feedforward latent scene decoders typically output volumetric 3D Gaussians that lack a well-defined surface, limiting their use in simulation or standard graphics pipelines. This motivates decoding surface-aligned primitives that are not only renderable but also closer to explicit geometric assets. We ask whether compressed video diffusion latents can be mapped directly to explicit surface primitives in a single pass. To this end, we introduce FLAT and, for the first time, show that triangle splats can be decoded directly from video diffusion latents. Compared with decoding 3D Gaussians, predicting flat primitives is notoriously more challenging due to high sensitivity to primitive orientations, oftentimes leading to poor gradient flow. FLAT solves with two key ingredients: a ray-centered rotation parameterization for triangle regression and a novel product window function that improves gradient flow during differentiable triangle rendering. On standard benchmarks, FLAT achieves significantly better geometric accuracy while maintaining competitive visual quality compared to state-of-the-art feedforward baselines. We further show that a lightweight test-time refinement step converts the predicted triangle soup into a fully opaque, game-engine-ready representation that supports real-time rendering. By evaluating 3DGS, 2DGS, and triangle splatting variants under an identical training setup, we provide the first systematic analysis of representation tradeoffs in feedforward scene generation. The project page is available at https://flat-splat.github.io